MR-based radiomics-clinical nomogram in epithelial ovarian tumor prognosis prediction: tumor body texture analysis across various acquisition protocols

Background Epithelial ovarian cancer (EOC) is the most malignant gynecological tumor in women. This study aimed to construct and compare radiomics-clinical nomograms based on MR images in EOC prognosis prediction. Methods A total of 186 patients with pathologically proven EOC were enrolled and randomly divided into a training cohort (n = 130) and a validation cohort (n = 56). Clinical characteristics of each patient were retrieved from the hospital information system. A total of 1116 radiomics features were extracted from tumor body on T2-weighted imaging (T2WI), T1-weighted imaging (T1WI), diffusion weighted imaging (DWI) and contrast-enhanced T1-weighted imaging (CE-T1WI). Paired sequence signatures were constructed, selected and trained to build a prognosis prediction model. Radiomic-clinical nomogram was constructed based on multivariate logistic regression analysis with radiomics score and clinical features. The predictive performance was evaluated by receiver operating characteristic curve (ROC) analysis, decision curve analysis (DCA) and calibration curve. Results The T2WI radiomic-clinical nomogram achieved a favorable prediction performance in the training and validation cohort with an area under ROC curve (AUC) of 0.866 and 0.818, respectively. The DCA showed that the T2WI radiomic-clinical nomogram was better than other models with a greater clinical net benefit. Conclusion MR-based radiomics analysis showed the high accuracy in prognostic estimation of EOC patients and could help to predict therapeutic outcome before treatment.

Analysis of Autophagy-Related Signatures in The Tumor Immune Microenvironment and Identification of Clinical Prognostic Markers in Bladder Carcinoma

Objectives: Bladder carcinoma (BLCA) is one of the most common malignant diseases of urinary system. Our study aimed to investigate the autophagy-related signatures in the tumor immune microenvironment and construct effective prognosis prediction model.Methods: RNA sequencing data and corresponding clinical information of BLCA were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Autophagy-related genes were extracted from TCGA dataset for consensus clustering analysis, and differences in survival rate were analyzed. STIMATE algorithm was used to analyze the tumor microenvironment (TME) and immune cell infiltration was compared between different clusters. Differentially expressed genes (DEGs) between different clusters were identified, followed by function annotation. Independent prognostic signatures were further revealed to construct prognostic prediction model.Results: We identified 35 autophagy-related genes associated with prognosis. Survival rate of samples in cluster 1 was significant lower than that in cluster 2. Cluster 2 had markedly lower tumor purity and significantly higher estimate score and stromal score than cluster 1. The proportions of T cells CD8, macrophages M1, T cells CD4 memory activated, NK cells activated, and dendritic cells activated were higher in cluster 1. There were 1,275 DEGs which were mainly enriched in functions and pathways related to immune response and cancer. Seven genes (ATF6, CAPN2, NAMPT, NPC1, P4HB, PIK3C3, and RPTOR) were further identified as the independent prognostic signatures to construct risk score prediction model, which had good prediction performance.Conclusion: Prognosis prediction model based on 7 autophagy-related genes may have great value in BLCA prognosis prediction.

Multimodal Monitoring in Large Hemispheric Infarction: Quantitative Electroencephalography Combined With Transcranial Doppler for Prognosis Prediction

Background: We aimed to explore whether transcranial Doppler (TCD) combined with quantitative electroencephalography (QEEG) can improve prognosis evaluation in patients with a large hemispheric infarction (LHI) and to establish an accurate prognosis prediction model.Methods: We prospectively assessed 90-day mortality in patients with LHI. Brain function was monitored using TCD-QEEG at the bedside of the patient.Results: Of the 59 (55.3 ± 10.6 years; 17 men) enrolled patients, 37 (67.3%) patients died within 90 days. The Cox regression analyses revealed that the Glasgow Coma Scale (GCS) score ≤ 8 [hazard ratio (HR), 3.228; 95% CI, 1.335–7.801; p = 0.009], TCD-terminal internal carotid artery as the offending vessel (HR, 3.830; 95% CI, 1.301–11.271; p = 0.015), and QEEG-a (delta + theta)/(alpha + beta) ratio ≥ 3 (HR, 3.647; 95% CI, 1.170–11.373; p = 0.026) independently predicted survival duration. Combining these three factors yielded an area under the receiver operating characteristic curve of 0.905 and had better predictive accuracy than those of individual variables (p < 0.05).Conclusion: TCD and QEEG complement the GCS score to create a reliable multimodal method for monitoring prognosis in patients with LHI.

Establishment of a prognosis Prediction Model Based on Pyroptosis-Related Signatures Associated With the Immune Microenvironment and Molecular Heterogeneity in Clear Cell Renal Cell Carcinoma

BackgroundPyroptosis is essential for tumorigenesis and progression of neoplasm. However, the heterogeneity of pyroptosis and its relationship with the tumor microenvironment (TME) in clear cell renal cell carcinoma (ccRCC) remain unclear. The purpose of the present study was to identify pyroptosis-related subtypes and construct a prognosis prediction model based on pyroptosis signatures.MethodsFirst, heterogenous pyroptosis subgroups were explored based on 33 pyroptosis-related genes and ccRCC samples from TCGA, and the model established by LASSO regression was verified by the ICGC database. Then, the clinical significance, functional status, immune infiltration, cell–cell communication, genomic alteration, and drug sensitivity of different subgroups were further analyzed. Finally, the LASSO-Cox algorithm was applied to narrow down the candidate genes to develop a robust and concise prognostic model.ResultsTwo heterogenous pyroptosis subgroups were identified: pyroptosis-low immunity-low C1 subtype and pyroptosis-high immunity-high C2 subtype. Compared with C1, C2 was associated with a higher clinical stage or grade and a worse prognosis. More immune cell infiltration was observed in C2 than that in C1, while the response rate in the C2 subgroup was lower than that in the C1 subgroup. Pyroptosis-related genes were mainly expressed in myeloid cells, and T cells and epithelial cells might influence other cell clusters via the pyroptosis-related pathway. In addition, C1 was characterized by MTOR and ATM mutation, while the characteristics of C2 were alterations in SPEN and ROS1 mutation. Finally, a robust and promising pyroptosis-related prediction model for ccRCC was constructed and validated.ConclusionTwo heterogeneous pyroptosis subtypes were identified and compared in multiple omics levels, and five pyroptosis-related signatures were applied to establish a prognosis prediction model. Our findings may help better understand the role of pyroptosis in ccRCC progression and provide a new perspective in the management of ccRCC patients.

Screening and discrimination of optimal prognostic genes for pancreatic cancer based on a prognostic prediction model

The prognosis of pancreatic cancer is poor because patients are usually asymptomatic in the early stage and the early diagnostic rate is low. Therefore, in this study, we aimed to identify potential prognosis-related genes in pancreatic cancer to improve diagnosis and the outcome of patients. The mRNA expression profile data from The Cancer Genome Atlas database and GSE79668, GSE62452, and GSE28735 datasets from Gene Expression Omnibus were downloaded. The prognosis-relevant genes and clinical factors were analyzed using Cox regression analysis and the optimal gene sets were screened using the Cox proportional model. Next, the Kaplan-Meier survival analysis was used to evaluate the relationship between risk grouping and patient prognosis. Finally, an optimal gene-based prognosis prediction model was constructed and validated using a test dataset to discriminate the model accuracy and reliability. The results showed that 325 expression variable genes were identified, and 48 prognosis-relevant genes and three clinical factors, including lymph node stage (pathologic N), new tumor, and targeted molecular therapy were preliminarily obtained. In addition, a gene set containing 16 optimal genes was identified and included FABP6, MAL, KIF19, and REG4, which were significantly associated with the prognosis of pancreatic cancer. Moreover, a prognosis prediction model was constructed and validated to be relatively accurate and reliable. In conclusion, a gene set consisting of 16 prognosis-related genes was identified and a prognosis prediction model was constructed, which is expected to be applicable in the clinical diagnosis and treatment guidance of pancreatic cancer in the future.

Development and Validation of an Autophagy-Related Signature for Head and Neck Squamous Cell Carcinoma

Introduction. HNSCC is the sixth most frequent type of malignant carcinoma with a low prognosis rate. In addition, autophagy is important in cancer development and progression. The purpose of this study is to investigate the potential significance of ARGs in the diagnosis and treatment of HNSCC. Materials and Methods. Expression data and clinical information of HNSCC samples were collected from the TCGA database, and a list of ARGs was obtained from the MSigDB. Then, we used R software to perform differential expression analysis and functional enrichment analysis. Further analysis was also performed to find out the survival-related ARGs in HNSCC, and two prognosis-related ARGs, FADD and NKX2-3, were selected to construct a prognosis prediction model. Moreover, some methods were applied to validate the prognosis prediction model. Finally, we used cell lines and clinical tissue samples of HNSCC to analyze the importance of FADD and NKX2-3. Results. We screened a total of 38 differentially expressed ARGs, and enrichment analysis showed that these genes were mainly involved in autophagy. Then, we selected FADD and NKX2-3 to construct a prognosis model and the risk score calculated by the model was proved to be effective in predicting the survival of HNSCC patients. Additionally, significant differences of the clinicopathological parameters could also be observed in the risk scores and the expression of NKX2-3 and FADD. The expression of FADD and NKX2-3 in cell lines and HNSCC tissue samples also showed the same trends. Conclusions. ARGs may be a potential biomarker for HNSCC prognosis, and targeted therapies for FADD and NKX2-3 are possible to be a new strategy of HNSCC treatment.

Establishment of a prognosis prediction model based on pyroptosis-related signatures associated with the immune microenvironment and molecular heterogeneity in clear cell renal carcinoma

Background: Pytoptosis is essential for tumorigenesis and progression of clear cell renal cell carcinoma (ccRCC). However, the heterogeneity of pyroposis and its relationship with the tumor microenvironment (TME) remain unclear. The aim of the present study was to identify proptosis-related subtypes and construct a prognosis prediction model based on pyroptosis signatures. Methods: First, heterogenous pyroptosis subgroups were explored based on 33 pyroptosis-related genes and ccRCC samples from TCGA, and the model establsihed by LASSO regression was verified by ICGC database. Then, the clinical significance, functional status, immune infiltration, cell-cell communication, genomic alteration and drug sensitivity of different subgroups were further analyzed. Finally, the LASSO-Cox algorithm was applied to narrow down the candidate genes to develop a robust and concise prognostic model. Results: Two heterogenous pyroptosis subgroups were identified: pyroptosis-low immunity-low C1 subtype, and pyroptosis-high immunity-high C2 subtype. Compared with C1, C2 was associated with a higher clinical stage or grade and a worse prognosis. More immune cell infiltration was observed in C2 than that in C1, while the response rate in C2 subgroup was lower than that in C1 subgroup. Pyroptosis related genes were mainly expressed in myeloid cells, and T cells and epithelial cells might influence other cell clusters via Pyroptosis related pathway. In addition, C1 was characterized by MTOR and ATM mutation, while C2 was characterized by more significant alterations in SPEN and ROS1 mutation. Finally, we constructed and validated a robust and promising signature based on the pyroptosis-related risk score for assessing the prognosis in ccRCC. Conclusion: We identified two heterogeneous pyroptosis subtypes and 5 reliable risk signatures to establish a prognosis prediction model. Our findings may help better understand the role of pyroptosis in ccRCC progression and provide a new perspective in the management of ccRCC patients.